add anonymized training traces (6000 records, 3 families x persistent/stateless)

.gitattributes

@@ -58,3 +58,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data/knapsack/persistent/traces.jsonl filter=lfs diff=lfs merge=lfs -text
+data/knapsack/stateless/traces.jsonl filter=lfs diff=lfs merge=lfs -text
+data/navigation/persistent/traces.jsonl filter=lfs diff=lfs merge=lfs -text
+data/navigation/stateless/traces.jsonl filter=lfs diff=lfs merge=lfs -text
+data/rule_diagnosis/persistent/traces.jsonl filter=lfs diff=lfs merge=lfs -text
+data/rule_diagnosis/stateless/traces.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,77 @@
+---
+license: apache-2.0
+task_categories:
+- text-generation
+language:
+- en
+tags:
+- code-generation
+- tool-use
+- agent
+- knapsack
+- navigation
+- rule-diagnosis
+- codeact
+- runtime-semantics
+pretty_name: "Training Traces"
+size_categories:
+- 1K<n<10K
+configs:
+- config_name: knapsack_persistent
+  data_files:
+  - split: train
+    path: data/knapsack/persistent/traces.jsonl
+  default: true
+- config_name: knapsack_stateless
+  data_files:
+  - split: train
+    path: data/knapsack/stateless/traces.jsonl
+- config_name: navigation_persistent
+  data_files:
+  - split: train
+    path: data/navigation/persistent/traces.jsonl
+- config_name: navigation_stateless
+  data_files:
+  - split: train
+    path: data/navigation/stateless/traces.jsonl
+- config_name: rule_diagnosis_persistent
+  data_files:
+  - split: train
+    path: data/rule_diagnosis/persistent/traces.jsonl
+- config_name: rule_diagnosis_stateless
+  data_files:
+  - split: train
+    path: data/rule_diagnosis/stateless/traces.jsonl
+---
+
+# Training Traces
+
+Anonymous supplementary release for a double-blind workshop submission. This dataset
+holds the teacher agent traces used to fine-tune the paper's LoRA adapters (see the
+sibling `cap-sweep-eval-data` release and the ten adapter repos alongside this one).
+
+6,000 agent traces total (1,000 per family x runtime combination), produced by an LLM
+teacher solving each of the paper's three agentic task families -- Opaque Knapsack,
+navigation, and rule diagnosis -- under two interpreter runtime conditions. Each config
+is a persistent/stateless pair for one family; all six were used to fine-tune the
+correspondingly-named LoRA adapters.
+
+## Key terms
+
+- **Persistent runtime**: the Python interpreter keeps all variables alive between
+  agent steps. An agent can write `total_weight += w` and it persists to the next turn.
+- **Stateless runtime**: the interpreter resets after every step; nothing carries over
+  automatically, so the agent must re-establish any state it needs each turn.
+
+## Format
+
+Each line is a `{"messages": [...]}` chat-format record (system / user / assistant /
+tool turns) suitable for direct SFT ingestion. Records include the full CodeAct-style
+system prompt, task prompt, the agent's code blocks and tool outputs (including tool
+exceptions), through to task completion.
+
+## Provenance
+
+Released anonymously alongside a NeurIPS workshop submission for reproducibility
+review. Non-anonymous release (paper citation, full code) will follow after the review
+process concludes.

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